Roughly 2 percent of the 200,000 bitcoin transactions in the dataset were evaluated as illicit. Meanwhile the 21 percent was identified as lawful, but most of the crypto transactions, more than 77 percent, was left unclassified.

To make it clear, the 2 percent comes from an Elliptic data set that was first not public and that data was merely affirmed by MIT researcher’s analysis. Although, the data is pretty similar to those published by competing company Chainalysis, which assessed just 1 percent of bitcoin payments and transactions was somehow associated with illegal activities.

Since Elliptic is very often hired by law enforcement agencies all around the globe to identify reveal illegal activities using cryptocurrencies, this research was aiming to identify models that can eventually help to distinguish illicit usage from lawful bitcoin usage, mostly among unbanked individuals.

“A big struggle with compliance, in general, is false positives. Big part of this study is minimizing the number of false positives.” Co-founder of Elliptic, Tom Robinson told CoinDesk. “The key finding is that machine learning techniques are very effective at finding transactions that are illicit.”

Sometimes, Robinson added, the software was able to find some patterns that would be very hard to describe yet still matched with known entities, based on before-existing data from darknet markets, ransomware attacks and other criminal investigations.